• DocumentCode
    2243829
  • Title

    Studies on automatic recognition of preposition BA´s usages based on statistics

  • Author

    Lingling Mu ; Yiya Pang ; Hongying Zan

  • Author_Institution
    Sch. of Inf. Eng., Zhengzhou Univ., Zhengzhou, China
  • fYear
    2012
  • fDate
    Oct. 30 2012-Nov. 1 2012
  • Firstpage
    1393
  • Lastpage
    1397
  • Abstract
    Researching on automatic recognition of preposition´s usages is one of the important contents of the Chinese function words Knowledge Corpus. Aiming at the low recognition probability problem for some high-frequency usages with rule-based method of preposition´ usages recognition, this paper proposed automatically recognizing of preposition´ usages based on statistics. Three statistical models, that is CRF, ME and SVM, were used to label preposition BA´s usages on the tagged corpus of People´s Daily (February, March, April of 2000). The experiment of statistical methods was done for preposition BA. And the final results showed that in general automatic recognition of preposition BA´s usages based on statistics was better than that of rule-based method of preposition BA´s usages recognition.
  • Keywords
    knowledge based systems; maximum entropy methods; natural language processing; probability; random processes; statistical analysis; statistics; support vector machines; text analysis; word processing; CRF; Chinese function word knowledge corpus; SVM; automatic preposition BA usage recognition; conditional random field; high-frequency usages; low recognition probability problem; maximum entropy; rule-based preposition usage recognition method; statistical methods; statistical models; support vector machine; Accuracy; Barium; Context; Dictionaries; Speech; Support vector machines; Testing; automatic recognition; chinese function work; conditional random fields; maximum entropy; usage description;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4673-1855-6
  • Type

    conf

  • DOI
    10.1109/CCIS.2012.6664614
  • Filename
    6664614